Method and system for determining moving speed of endoscope camera in gastrointestinal tract

ABSTRACT

A method includes steps of: based on training sets of gastrointestinal images, using a predetermined machine learning algorithm to obtain a preliminary model; feeding preliminary validation sets of gastrointestinal images into the preliminary model to obtain estimation results; based on the estimation results, selecting, from the preliminary validation sets of gastrointestinal images, a series of successive images as a selected validation set of gastrointestinal images; based on the selected validation set of gastrointestinal images, tuning parameters of the preliminary model to result in a speed-determining model for determining a moving speed of an endoscope camera.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority of Taiwanese Invention PatentApplication No. 110143933, filed on Nov. 25, 2021.

FIELD

The disclosure relates to a method and a system for determining a movingspeed of an endoscope camera in a gastrointestinal tract.

BACKGROUND

During an endoscopy, a medical practitioner often operates an endoscopewith the assistance of images that are captured by the endoscope in realtime and that are displayed on a screen. However, operation of theendoscope is still based on the medical practitioner's experience andexpertise, and it is difficult for the medical practitioner to know amoving speed of a distal tip of the endoscope and hence difficult tomove the distal tip at a proper speed in, for example, a large intestineof a patient.

SUMMARY

Therefore, an object of the disclosure is to provide a method and asystem for determining a moving speed of an endoscope camera in agastrointestinal tract that can alleviate at least one of the drawbacksof the prior art.

According to one aspect of the disclosure, the method is to beimplemented by a processor, and the method includes steps of:

-   -   based on a plurality of training sets of gastrointestinal images        captured by a camera module moving in an artificial        gastrointestinal tract respectively at a plurality of preset        moving speeds and the preset moving speeds, using a        predetermined machine learning algorithm to obtain a preliminary        model for determining a moving speed of the camera module, the        training sets of gastrointestinal images being captured with a        preset frame rate during a preset time period;    -   feeding a plurality of preliminary validation sets of        gastrointestinal images of a real gastrointestinal tract into        the preliminary model to obtain a plurality of estimation        results that respectively correspond to the preliminary        validation sets of gastrointestinal images, each of the        estimation results including at least one estimated speed that        corresponds to a series of successive images included in the        corresponding one of the preliminary validation sets of        gastrointestinal images;    -   based on the estimation results, selecting, from the preliminary        validation sets of gastrointestinal images, at least one series        of successive images corresponding to an estimated speed that is        substantially equal to one of the preset moving speeds        respectively as at least one selected validation set of        gastrointestinal images;    -   based on the at least one selected validation set of        gastrointestinal images, tuning parameters of the preliminary        model to result in a speed-determining model for determining a        moving speed of an endoscope camera in a gastrointestinal tract;    -   when receiving a target set of gastrointestinal images that are        successively captured by the endoscope camera moving in a target        gastrointestinal tract, using the speed-determining model to        determine, based on the target set of gastrointestinal images, a        moving speed of the endoscope camera in the target        gastrointestinal tract; and    -   outputting the moving speed of the endoscope camera thus        determined.

According to another aspect of the disclosure, the system includes aconnecting interface, a storage medium, an output unit and a processor.

The connecting interface is electrically connected to the endoscopecamera.

The storage medium is configured to store a speed-determining model thatis established using the method previously described.

The processor is electrically connected to the connecting interface, thestorage medium and the output unit. The processor is configured toreceive, via the connecting interface, a target set of gastrointestinalimages that are successively captured by the endoscope camera moving ina target gastrointestinal tract, based on the target set ofgastrointestinal images, to use the speed-determining model to determinea moving speed of the endoscope camera in the target gastrointestinaltract, to output, via the output unit, the moving speed of the endoscopecamera thus determined, to determine whether the moving speed of theendoscope camera is greater than a first predetermined speed threshold,and when it is determined that the moving speed of the endoscope camerais greater than the first predetermined speed threshold, to output, viathe output unit, a first notification which indicates that the endoscopecamera is moving extremely fast.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features and advantages of the disclosure will become apparent inthe following detailed description of the embodiment with reference tothe accompanying drawings, of which:

FIG. 1 is a block diagram illustrating a system for determining a movingspeed of an endoscope camera in a gastrointestinal tract according to anembodiment of the disclosure;

FIG. 2 is a flow chart illustrating a method of establishing aspeed-determining model according to an embodiment of the disclosure;

FIG. 3 is a flow chart illustrating a speed-determining procedureimplemented by the system according to an embodiment of the disclosure;and

FIG. 4 is a schematic diagram illustrating an example of a userinterface provided by the system according to an embodiment of thedisclosure.

DETAILED DESCRIPTION

Before the disclosure is described in greater detail, it should be notedthat where considered appropriate, reference numerals or terminalportions of reference numerals have been repeated among the figures toindicate corresponding or analogous elements, which may optionally havesimilar characteristics.

Referring to FIG. 1 , an embodiment of a system 100 for determining amoving speed of an endoscope camera 200 in a gastrointestinal tractaccording to the disclosure is illustrated. For example, the endoscopecamera is provided at a distal end of an endoscope (e.g., acolonoscope), and the gastrointestinal tract is exemplarily the largeintestine (colon) and rectum, but application of the system 100 is notlimited to what are disclosed herein and may vary in other embodiments.

The system 100 includes a connecting interface 1, a processor 2, astorage medium 3 and an output unit 4. The processor 2 is electricallyconnected to the connecting interface 1, the storage medium 3 and theoutput unit 4.

The connecting interface 1 is electrically connected to the endoscopecamera 200. The connecting interface 1 is implemented to be anelectrical connector (which may support standards of universal serialbus, USB), a network interface controller or a wireless transceiver thatsupports wireless communication standards, such as Bluetooth® technologystandards, Wi-Fi technology standards and/or cellular network technologystandards, but is not limited thereto.

The storage medium 3 is configured to store a speed-determining model.The speed-determining model is used for determining a moving speed ofthe endoscope camera 200 in a gastrointestinal tract.

The storage medium 3 may be implemented by random access memory (RAM),double data rate synchronous dynamic random access memory (DDR SDRAM),read only memory (ROM), programmable ROM (PROM), flash memory, a harddisk drive (HDD), a solid state disk (SSD), electrically-erasableprogrammable read-only memory (EEPROM) or any othervolatile/non-volatile memory devices, but is not limited thereto.

The output unit 4 includes a display 41 and a speaker 42. Each of thedisplay 41 and the speaker 42 is electrically connected to the processor2. The display 41 may be a liquid-crystal display (LCD), alight-emitting diode (LED) display, a plasma display panel, a projectiondisplay or the like. However, implementation of the display 41 is notlimited to the disclosure herein and may vary in other embodiments. Theoutput unit 4 is configured to provide a user interface as shown in FIG.4 . The user interface includes a speed-displaying region 411, animage-displaying region 412, and four abnormality-displaying regions 413that are positioned respectively at four corners of the image-displayingregion 412.

The processor 2 may be implemented by a central processing unit (CPU), amicroprocessor, a micro control unit (MCU), a system on a chip (SoC), orany circuit configurable/programmable in a software manner and/orhardware manner to implement functionalities discussed in thisdisclosure.

The processor 2 is configured to receive, via the connecting interface1, a target set of gastrointestinal images that are successivelycaptured by the endoscope camera 200 moving in a target gastrointestinaltract (e.g., a real large intestine of a patient). Based on the targetset of gastrointestinal images, the processor 2 is configured to use thespeed-determining model stored in the storage medium 3 to determine amoving speed of the endoscope camera 200 in the target gastrointestinaltract, and to output, via the output unit 4, the moving speed of theendoscope camera 200 thus determined.

In addition, the processor 2 is configured to determine whether themoving speed of the endoscope camera 200 is greater than a firstpredetermined speed threshold (e.g., 20 mm/sec), and when it isdetermined that the moving speed of the endoscope camera 200 is greaterthan the first predetermined speed threshold, to output, via the outputunit 4, a first notification which indicates that the endoscope camera200 is moving extremely fast.

Moreover, the processor 2 is configured to determine whether the movingspeed of the endoscope camera 200 is greater than a second predeterminedspeed threshold (e.g., 15 mm/sec) that is smaller than the firstpredetermined speed threshold when it is determined that the measuredmoving speed of the endoscope camera 200 is not greater than the firstpredetermined speed threshold. When it is determined that the movingspeed of the endoscope camera 200 is greater than the secondpredetermined speed threshold, the processor 2 is configured to output,via the output unit 4, a second notification which indicates that theendoscope camera 200 is moving a bit fast.

In one embodiment, the processor 2 is configured to control the display41 to display one of the moving speed of the endoscope camera 200, thefirst notification, the second notification and a combination thereof.

Specifically, the moving speed is outputted via the display 41 in theform of a first visual output (e.g., a combination of a graphical speedmeter and a numerical value), the first notification is outputted viathe display 41 in the form of a second visual output (e.g., a sign“Warning!” with a red background), and the second notification isoutputted via the display 41 in the form of a third visual output (e.g.,a warning sign “Warning!” with a yellow background).

In particular, referring to FIG. 4 , the moving speed is presented inthe speed-displaying region 411 of the user interface provided by thedisplay 41 in real time as the target set of gastrointestinal images arebeing received from the endoscope camera 200, and each of the firstnotification and the second notification is also presented in thespeed-displaying region 411. At the same time, each gastrointestinalimage in the target set of gastrointestinal images is presented in theimage-displaying region 412 of the user interface in real time.

In one embodiment, the processor 2 is configured to control the display41 to display the moving speed of the endoscope camera 200, and tocontrol the speaker 42 to output the first notification in the form of afirst audio output (e.g., a high-frequency sound) and output the secondnotification in the form of a second audio output (e.g., a low-frequencysound). In this way, a medical practitioner can be effectively notifiedby the system 100 according to the disclosure of the moving speed of theendoscope camera 200 as he/she is operating the endoscope camera 200.

In one embodiment, the storage medium 3 is configured to further store areference image, and the processor 2 is further configured to perform ablur-determining procedure on each gastrointestinal image in the targetset of gastrointestinal images before using the speed-determining modelto determine a moving speed of the endoscope camera 200 based on thetarget set of gastrointestinal images. Specifically, the processor 2 isconfigured to determine whether the gastrointestinal image is blurry bycomparing the gastrointestinal image with the reference image stored inthe storage medium 3. When it is determined that the gastrointestinalimage is non-blurry, the processor 2 is configured to reserve thegastrointestinal image in the target set of gastrointestinal images fordetermining the moving speed of the endoscope camera 200 by using thespeed-determining model. On the other hand, when it is determined thatthe gastrointestinal image is blurry, the processor 2 is configured toremove the gastrointestinal image from the target set ofgastrointestinal images.

In order to ensure that the determination made by the speed-determiningmodel is relatively accurate, before outputting the moving speed, theprocessor 2 is further configured to estimate an estimated speed of theendoscope camera 200 moving in the target gastrointestinal tract using amultiscale structural similarity index measure (MS-SSIM) based on thetarget set of gastrointestinal images with all images therein havingbeen determined as non-blurry. Then, the processor 2 is configured toadjust the moving speed based on the estimated speed.

In one embodiment, the storage medium 3 is configured to further store adetecting model. The detecting model has been trained in advance byusing a machine learning algorithm (e.g., an algorithm belonging to“EfficientNet” model family) based on normal and abnormalgastrointestinal images. Each of the abnormal gastrointestinal images isrelated to one of cancer, diverticulitis, ileocecal valve, and so on.The detecting model includes an input layer for receiving an inputimage, a plurality of hidden layers, and an output layer for outputtinga result of determination that is made based on the input image and thatis presented in a form of a probability of being normal or abnormal. Forexample, when a gastrointestinal image is fed via the input layer intothe detecting model, the detecting model may output via the output layera result of determination made based on the gastrointestinal image toindicate that the probability of being normal is 6.12 percent or theprobability of being abnormal is 93.88 percent, meaning that the resultof determination indicates that it is highly likely that there is anabnormal condition in the target gastrointestinal tract based on thegastrointestinal image.

For each gastrointestinal image included in the target set ofgastrointestinal images, the processor 2 is further configured todetermine, based on the gastrointestinal image, whether there is anyabnormal condition (i.e., cancer, diverticulitis, or ileocecal valve) inthe target gastrointestinal tract using the detecting model, and tooutput an abnormal indication via the output unit 4 when it isdetermined that there is an abnormal condition in the targetgastrointestinal tract. Taking the above-mentioned result ofdetermination made by the detecting model (i.e., the probability ofbeing normal is 6.12 percent and the probability of being abnormal is93.88 percent) as an example, the processor 2 is then configured todetermine that there may be an abnormal condition in the targetgastrointestinal tract.

As the target set of gastrointestinal images are being displayed by thedisplay 41, the processor 2 is configured to control the display 41 todisplay the abnormal indication in the form of a fourth visual output(e.g., coloring the four abnormality-displaying regions 413 of the userinterface in red), and to control the speaker 42 to output the abnormalindication in the form of a third audio output (e.g., a high-frequencysound different from the first audio output). In this way, a medicalpractitioner can be effectively notified in time by the system 100according to the disclosure that there is an abnormal condition in thetarget gastrointestinal tract.

Referring to FIG. 2 , a method of establishing a speed-determining modelfor determining a moving speed of an endoscope camera in agastrointestinal tract according to the disclosure is illustrated. Themethod is implemented by a processor (which may be the processor 2 aspreviously described, or a processor other than the processor 2), acamera module (which may be the endoscope camera 200 as previouslydescribed, or an endoscope camera other than the endoscope camera 200),an artificial gastrointestinal tract (not shown) and a driving device(e.g., a motor, not shown). The method includes steps S21 to S26delineated below.

In step S21, the driving device drives the camera module to move in theartificial gastrointestinal tract multiple times at a plurality ofpreset moving speeds. For example, the preset moving speeds includetwenty speeds ranging from one mm/sec to twenty mm/sec in one-mm/secincrements (i.e., 1 mm/sec, 2 mm/sec, and 20 mm/sec).

In step S22, the camera module captures a plurality of training sets ofgastrointestinal images with a preset frame rate (e.g., thirty FPS)during a preset time period (e.g., six seconds). It should be noted thatthe training sets of gastrointestinal images are captured by the cameramodule moving respectively at the preset moving speeds.

For example, twenty training sets of gastrointestinal images arecaptured by the camera module moving respectively at the twenty speeds(i.e., 1 mm/sec, 2 mm/sec, and 20 mm/sec), and for each of the twentytraining sets of gastrointestinal images, image capturing is performedat the frame rate of thirty FPS for six seconds in order to obtaingastrointestinal images in the training set of gastrointestinal images.Accordingly, each of the twenty training sets of gastrointestinal imagesincludes 180 gastrointestinal images.

In step S23, based on the training sets of gastrointestinal images andthe corresponding preset moving speeds, the processor uses apredetermined machine learning algorithm (e.g., a “3D ResNet-18” model)to obtain a preliminary model for determining a moving speed of thecamera module.

In step S24, the processor feeds a plurality of preliminary validationsets of gastrointestinal images of a real gastrointestinal tract intothe preliminary model to obtain a plurality of estimation results thatrespectively correspond to the preliminary validation sets ofgastrointestinal images. Each of the estimation results includes atleast one estimated speed that corresponds to a series of successiveimages which are included in the corresponding one of the preliminaryvalidation sets of gastrointestinal images. That is to say, theestimated speed is obtained by the preliminary model based on the seriesof successive images included in the corresponding one of thepreliminary validation sets of gastrointestinal images. For example, theseries of successive images may be 180 successive images and thecorresponding preliminary validation set of gastrointestinal imagesincludes any number of images greater than or equal to 180. In somecases where one of the estimation results includes multiple estimatedspeeds, the estimated speeds correspond respectively to multiple seriesof successive images included in the corresponding one of thepreliminary validation sets of gastrointestinal images. It is worthnoting that the preliminary validation sets of gastrointestinal imagesare captured by the camera module with the preset frame rate (i.e.,thirty FPS).

In step S25, based on the estimation results, the processor selects,from the preliminary validation sets of gastrointestinal images, atleast one series of successive images corresponding to an estimatedspeed that is substantially equal to one of the preset moving speedsrespectively as at least one selected validation set of gastrointestinalimages. It is worth to note that each of the at least one selectedvalidation set includes an identical total number of images (e.g., 180gastrointestinal images). In this way, a sufficient number of series ofsuccessive images may be obtained for refining the preliminary model toenhance accuracy of determining a moving speed of an endoscope camera.

For example, the processor would select, from the preliminary validationsets of gastrointestinal images, at least one series of successiveimages corresponding to an estimated speed that is substantially equalto one of the twenty speeds (i.e., one of 1 mm/sec, 2 mm/sec, . . . ,and 20 mm/sec).

In step S26, based on the at least one selected validation set ofgastrointestinal images, the processor tunes parameters of thepreliminary model to result in the speed-determining model.

Hereinafter, one target set of gastrointestinal images that aresuccessively captured by the endoscope camera 200 moving in a targetgastrointestinal tract are considered as one unit, and one unitcontains, for example, thirty gastrointestinal images. It is worthnoting that the target set of gastrointestinal images are captured bythe camera module with the preset frame rate (i.e., thirty FPS).Referring to FIG. 3 , for each unit of gastrointestinal images, thesystem 100 performs, on the unit, a speed-determining procedure thatincludes steps S31 to S35 delineated below.

In step S31, the processor 2 of the system 100 receives the unit via theconnecting interface 1 from the endoscope camera 200.

In step S32, the processor 2 performs a blur-determining procedure oneach gastrointestinal image in the unit.

More specifically, for each gastrointestinal image in the unit, theprocessor 2 determines whether the gastrointestinal image is blurry bycomparing the gastrointestinal image with the reference image. When itis determined that the gastrointestinal image is non-blurry, theprocessor 2 reserves the gastrointestinal image in the unit. When it isdetermined that the gastrointestinal image is blurry, the processor 2removes the gastrointestinal image from the unit.

In this way, all images left in the unit are determined as non-blurry atthe end of step S32. Then, the unit will be utilized by the system 100in steps S33 and S34 separately.

In step S33, based on the gastrointestinal images included in the unit,the processor 2 uses the speed-determining model to determine a movingspeed of the endoscope camera 200 in the target gastrointestinal tract.

In step S34, the processor 2 estimates an estimated speed of theendoscope camera 200 moving in the target gastrointestinal tract usingthe MS-SSIM based on the gastrointestinal images included in the unit.

In step S35, the processor 2 adjusts the moving speed obtained in stepS33 based on the estimated speed obtained in step S34. After that, theprocessor 2 outputs, via the output unit 4, the moving speed of theendoscope camera 200 thus adjusted (hereinafter also referred to as theadjusted moving speed).

For example, in a scenario where the estimated speed obtained by usingMS-SSIM is 9 mm/sec and the moving speed obtained by using thespeed-determining model is 5 mm/sec, the processor 2 multiplies theestimated speed by a first predefined weight (e.g., 0.5) to result in afirst product, multiplies the moving speed by a second predefined weight(e.g., 0.5) to result in a second product, and makes a sum of the firstproduct and the second product the adjusted moving speed, e.g.,9×0.5+5×0.5=7 mm/sec.

To sum up, the speed-determining model established by using the methodaccording to the disclosure can be utilized by the system 100 accordingto the disclosure to monitor moving speed of an endoscope camera in agastrointestinal tract, and to notify a medical practitioner when theendoscope camera is moving too fast (i.e., exceeding the first and/orsecond predetermined speed threshold). In this way, the medicalpractitioner may be able to move the endoscope camera at the properspeed, and hence endoscopy may be performed in a relatively effectiveway. Moreover, the multiscale structural similarity index measure isutilized to further enhance accuracy of the moving speed determined byusing the speed-determining model. In addition, the system 100 willnotify the medical practitioner when it is determined by using thedetecting model that there is an abnormal condition (e.g., cancer,diverticulitis, or ileocecal valve) in the gastrointestinal tract.Consequently, the medical practitioner may be able to timely deal withthe abnormal condition.

In the description above, for the purposes of explanation, numerousspecific details have been set forth in order to provide a thoroughunderstanding of the embodiment. It will be apparent, however, to oneskilled in the art, that one or more other embodiments may be practicedwithout some of these specific details. It should also be appreciatedthat reference throughout this specification to “one embodiment,” “anembodiment,” an embodiment with an indication of an ordinal number andso forth means that a particular feature, structure, or characteristicmay be included in the practice of the disclosure. It should be furtherappreciated that in the description, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure and aiding in theunderstanding of various inventive aspects, and that one or morefeatures or specific details from one embodiment may be practicedtogether with one or more features or specific details from anotherembodiment, where appropriate, in the practice of the disclosure.

While the disclosure has been described in connection with what isconsidered the exemplary embodiment, it is understood that thisdisclosure is not limited to the disclosed embodiment but is intended tocover various arrangements included within the spirit and scope of thebroadest interpretation so as to encompass all such modifications andequivalent arrangements.

What is claimed is:
 1. A method for determining a moving speed of anendoscope camera in a gastrointestinal tract, to be implemented by aprocessor, the method comprising steps of: based on a plurality oftraining sets of gastrointestinal images captured by a camera modulemoving in an artificial gastrointestinal tract respectively at aplurality of preset moving speeds and the preset moving speeds, using apredetermined machine learning algorithm to obtain a preliminary modelfor determining a moving speed of the camera module, the training setsof gastrointestinal images being captured with a preset frame rateduring a preset time period; feeding a plurality of preliminaryvalidation sets of gastrointestinal images of a real gastrointestinaltract into the preliminary model to obtain a plurality of estimationresults that respectively correspond to the preliminary validation setsof gastrointestinal images, each of the estimation results including atleast one estimated speed that corresponds to a series of successiveimages included in the corresponding one of the preliminary validationsets of gastrointestinal images; based on the estimation results,selecting, from the preliminary validation sets of gastrointestinalimages, at least one series of successive images corresponding to anestimated speed that is substantially equal to one of the preset movingspeeds respectively as at least one selected validation set ofgastrointestinal images; based on the at least one selected validationset of gastrointestinal images, tuning parameters of the preliminarymodel to result in a speed-determining model for determining a movingspeed of an endoscope camera in a gastrointestinal tract; when receivinga target set of gastrointestinal images that are successively capturedby the endoscope camera moving in a target gastrointestinal tract, usingthe speed-determining model to determine, based on the target set ofgastrointestinal images, a moving speed of the endoscope camera in thetarget gastrointestinal tract; and outputting the moving speed of theendoscope camera thus determined.
 2. The method as claimed in claimprior to the step of determining a moving speed of the endoscope camera,the method further comprising a step of performing a blur-determiningprocedure on each gastrointestinal image in the target set ofgastrointestinal images by: determining whether the gastrointestinalimage is blurry by comparing the gastrointestinal image with a referenceimage; when it is determined that the gastrointestinal image isnon-blurry, reserving the gastrointestinal image in the target set ofgastrointestinal images for subsequent determination of the moving speedof the endoscope camera by using the speed-determining model; and whenit is determined that the gastrointestinal image is blurry, removing thegastrointestinal image from the target set of gastrointestinal images.3. The method as claimed in claim 2, subsequent to the step ofperforming a blur-determining procedure, the method further comprisingsteps of: estimating an estimated speed of the endoscope camera movingin the target gastrointestinal tract using a structural similarity indexmeasure based on the target set of gastrointestinal images, all imagesin which have been determined as non-blurry; and before the step ofoutputting the moving speed, adjusting the moving speed based on theestimated speed.
 4. The method as claimed in claim 1, further comprisingsteps of: determining whether the moving speed of the endoscope camerais greater than a first predetermined speed threshold; when it isdetermined that the moving speed of the endoscope camera is greater thanthe first predetermined speed threshold, outputting a first notificationwhich indicates that the endoscope camera is moving extremely fast. 5.The method as claimed in claim 4, further comprising steps of:determining whether the moving speed of the endoscope camera is greaterthan a second predetermined speed threshold that is smaller than thefirst predetermined speed threshold when it is determined that themeasured moving speed of the endoscope camera is not greater than thefirst predetermined speed threshold; when it is determined that themoving speed of the endoscope camera is greater than the secondpredetermined speed threshold, outputting a second notification whichindicates that the endoscope camera is moving a bit fast.
 6. The methodas claimed in claim 5, wherein the step of outputting the moving speedis to output the moving speed in a form of a first visual output, thestep of outputting the first notification is to output the firstnotification in a form of a second visual output, and the step ofoutputting the second notification is to output the second notificationin a form of a third visual output.
 7. The method as claimed in claim 5,to be implemented by a processor electrically connected to a display anda speaker, wherein the step of outputting the moving speed is to outputthe moving speed in a form of a visual output, the step of outputtingthe first notification is to output the first notification in a form ofa first audio output, and the step of outputting the second notificationis to output the second notification in a form of a second audio output.8. The method as claimed in claim 1, for each gastrointestinal imageincluded in the target set of gastrointestinal images, the methodfurther comprising steps of: based on the target set of gastrointestinalimages, determining whether there is any abnormal condition in thetarget gastrointestinal tract using a detecting model that has beentrained in advance by using a machine learning algorithm based on normaland abnormal gastrointestinal images; and outputting an abnormalindication when it is determined that there is an abnormal condition inthe target gastrointestinal tract.
 9. The method as claimed in claim 1,wherein: the preliminary validation sets of gastrointestinal images arecaptured by the camera module with the preset frame rate; and each ofthe at least one selected validation set includes an identical totalnumber of images.
 10. A system for determining a moving speed of anendoscope camera in a gastrointestinal tract, said system comprising: aconnecting interface electrically connected to the endoscope camera; astorage medium configured to store a speed-determining model that isestablished using the method of claim 1; an output unit; and a processorelectrically connected to said connecting interface, said storage mediumand said output unit, and configured to receive, via said connectinginterface, a target set of gastrointestinal images that are successivelycaptured by the endoscope camera moving in a target gastrointestinaltract, based on the target set of gastrointestinal images, use thespeed-determining model to determine a moving speed of the endoscopecamera in the target gastrointestinal tract, output, via said outputunit, the moving speed of the endoscope camera thus determined,determine whether the moving speed of the endoscope camera is greaterthan a first predetermined speed threshold, and when it is determinedthat the moving speed of the endoscope camera is greater than the firstpredetermined speed threshold, output, via said output unit, a firstnotification which indicates that the endoscope camera is movingextremely fast.
 11. The system as claimed in claim 10, wherein: saidstorage medium is further configured to store a reference image; andsaid processor is further configured to perform a blur-determiningprocedure on each gastrointestinal image in the target set ofgastrointestinal images by determining whether the gastrointestinalimage is blurry by comparing the gastrointestinal image with thereference image, when it is determined that the gastrointestinal imageis non-blurry, reserving the gastrointestinal image in the target set ofgastrointestinal images, and when it is determined that thegastrointestinal image is blurry, removing the gastrointestinal imagefrom the target set of gastrointestinal images, wherein said processoris configured to use the target set of gastrointestinal images resultingfrom the blur-determining procedure in determining the moving speed ofthe endoscope camera.
 12. The system as claimed in claim 11, whereinsaid processor is further configured to: estimate an estimated speed ofthe endoscope camera moving in the target gastrointestinal tract using astructural similarity index measure based on the target set ofgastrointestinal images, all images in which have been determined asnon-blurry; and adjust the moving speed based on the estimated speed.13. The system as claimed in claim 11, said processor is furtherconfigured to: determine whether the moving speed of the endoscopecamera is greater than a second predetermined speed threshold that issmaller than the first predetermined speed threshold when it isdetermined that the measured moving speed of the endoscope camera is notgreater than the first predetermined speed threshold; when it isdetermined that the moving speed of the endoscope camera is greater thanthe second predetermined speed threshold, output, via said output unit,a second notification which indicates that the endoscope camera ismoving slightly fast.
 14. The system as claimed in claim 13, wherein:said output unit includes a display that is electrically connected tosaid processor; and said processor is configured to control said displayto display one of the moving speed of the endoscope camera, the firstnotification, the second notification and a combination thereof.
 15. Thesystem as claimed in claim 13, wherein: said output unit includes aspeaker that is electrically connected to said processor; and saidprocessor is configured to control said speaker to output the firstnotification in a form of a first audio output and output the secondnotification in a form of a second audio output.
 16. The system asclaimed in claim 10, wherein: said storage medium is further configuredto store a detecting model that has been trained in advance by using amachine learning algorithm based on normal and abnormal gastrointestinalimages; and for each gastrointestinal image included in the target setof gastrointestinal images, said processor is further configured todetermine, based on the gastrointestinal image, whether there is anyabnormal condition in the target gastrointestinal tract using thedetecting model, and output an abnormal indication via said output unitwhen it is determined that there is an abnormal condition in the targetgastrointestinal tract.